app.py

import streamlit as st
from PIL import Image
import os
import base64
import io
from dotenv import load_dotenv
from groq import Groq
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from datetime import datetime
import re
from reportlab.lib import colors
import random
import streamlit.components.v1 as components

# ======================
# CONFIGURATION SETTINGS
# ======================
PAGE_CONFIG = {
    "page_title": "Radiology Analyzer",
    "page_icon": "🩺",
    "layout": "wide",
    "initial_sidebar_state": "expanded"
}

ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']

CSS_STYLES = """
<style>
    /* Main background and text colors */
    .main {
        background-color: #0e1117; 
        color: #ffffff;
    }
    .sidebar .sidebar-content {
        background-color: #1a1d24; 
        color: #ffffff;
    }
    
    /* Custom title styling */
    .main-title {
        font-size: 2.8rem; 
        font-weight: 700; 
        color: #ffffff; 
        margin-bottom: 0.2rem;
        text-align: center;
    }
    .sub-title { 
        font-size: 1.5rem; 
        color: #9ca3af; 
        margin-top: 0.2rem; 
        text-align: center;
        margin-bottom: 2rem;
    }
    
    /* Button styling */
    .stButton>button {
        background-color: #21b9e1 !important;
        color: white !important;
        border-radius: 8px !important;
        padding: 0.5rem 1rem !important;
        border: none !important;
        transition: all 0.3s ease !important;
    }
    .stButton>button:hover {
        background-color: #17a2b8 !important;
        transform: translateY(-2px);
        box-shadow: 0 4px 12px rgba(0, 200, 225, 0.3);
    }
    
    /* Report container */
    .report-container {
        background-color: #1a1d24;
        border-radius: 10px;
        padding: 25px;
        margin-top: 20px;
        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3);
        border-left: 5px solid #21b9e1;
    }
    .report-text {
        font-family: 'Inter', sans-serif;
        font-size: 14px;
        line-height: 1.6;
        color: #e2e8f0;
    }
    
    /* File uploader */
    .uploadedFile {
        background-color: #1a1d24 !important;
        border-radius: 10px !important;
        padding: 10px !important;
        border: 2px dashed #21b9e1 !important;
    }
    
    /* Sidebar items */
    .sidebar-item {
        padding: 10px 0;
        border-bottom: 1px solid #2d3748;
    }
    .sidebar-title {
        font-weight: bold;
        color: #21b9e1;
        margin-bottom: 10px;
    }
    
    /* Logo container */
    .logo-container {
        display: flex;
        justify-content: center;
        margin-bottom: 20px;
    }
    .logo-pulse {
        width: 100px;
        height: 100px;
        border-radius: 50%;
        animation: pulse 2s infinite;
        display: flex;
        justify-content: center;
        align-items: center;
        background-color: rgba(33, 185, 225, 0.1);
    }
    @keyframes pulse {
        0% {
            box-shadow: 0 0 0 0 rgba(33, 185, 225, 0.4);
        }
        70% {
            box-shadow: 0 0 0 20px rgba(33, 185, 225, 0);
        }
        100% {
            box-shadow: 0 0 0 0 rgba(33, 185, 225, 0);
        }
    }
    
    /* Progress bar */
    .stProgress > div > div {
        background-color: #21b9e1 !important;
    }
    
    /* Analysis status indicator */
    .analysis-complete {
        display: inline-flex;
        align-items: center;
        background-color: rgba(33, 225, 185, 0.2);
        color: #21e1b9;
        padding: 8px 16px;
        border-radius: 20px;
        font-weight: 600;
        margin-bottom: 20px;
    }
    .analysis-complete svg {
        margin-right: 8px;
    }
    
    /* Drop zone */
    .drop-zone {
        background-color: #1a1d24;
        border: 2px dashed #21b9e1;
        border-radius: 10px;
        padding: 40px 20px;
        text-align: center;
        transition: all 0.3s ease;
        margin-bottom: 20px;
    }
    .drop-zone:hover {
        border-color: #17a2b8;
        background-color: #242830;
    }
    .drop-icon {
        font-size: 3rem;
        color: #21b9e1;
        margin-bottom: 10px;
    }
    
    /* Markdown adjustments */
    .markdown-text-container p {
        color: #e2e8f0 !important;
    }
    
    /* Image styling */
    .stImage img {
        border-radius: 10px;
        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3);
        border: 3px solid #1a1d24;
    }
    
    /* Card styles */
    .card {
        background-color: #1a1d24;
        border-radius: 10px;
        padding: 20px;
        margin-bottom: 20px;
        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
        transition: all 0.3s ease;
    }
    .card:hover {
        transform: translateY(-5px);
        box-shadow: 0 10px 25px rgba(0, 0, 0, 0.3);
    }
    .card-title {
        color: #21b9e1;
        font-size: 1.2rem;
        font-weight: 600;
        margin-bottom: 10px;
    }
    
    /* Tooltip */
    .tooltip {
        position: relative;
        display: inline-block;
        cursor: pointer;
    }
    .tooltip .tooltiptext {
        visibility: hidden;
        width: 200px;
        background-color: #2d3748;
        color: #fff;
        text-align: center;
        border-radius: 6px;
        padding: 10px;
        position: absolute;
        z-index: 1;
        bottom: 125%;
        left: 50%;
        margin-left: -100px;
        opacity: 0;
        transition: opacity 0.3s;
    }
    .tooltip:hover .tooltiptext {
        visibility: visible;
        opacity: 1;
    }
    
    /* API selector styling */
    .api-selector {
        background-color: #1a1d24;
        border-radius: 10px;
        padding: 15px;
        margin-bottom: 20px;
        border-left: 3px solid #21b9e1;
    }
    .api-selector-title {
        color: #21b9e1;
        font-weight: bold;
        margin-bottom: 10px;
    }
</style>
"""

# ======================
# CORE FUNCTIONS
# ======================
def configure_application():
    """Initialize application settings and styling"""
    st.set_page_config(**PAGE_CONFIG)
    st.markdown(CSS_STYLES, unsafe_allow_html=True)

def initialize_groq_client():
    """Create and validate Groq API client"""
    load_dotenv()
    api_key = os.getenv("GROQ_API_KEY")        
    if not api_key:
        st.error("Groq API key not found. Please provide an API key.")
        return None
    
    return Groq(api_key=api_key)


def encode_logo(image_path):
    """Encode logo image to base64"""
    try:
        with open(image_path, "rb") as img_file:
            return base64.b64encode(img_file.read()).decode("utf-8")
    except FileNotFoundError:
        # Return a placeholder image (blue medical technology icon) encoded as base64
        return "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"

def process_image_data(uploaded_file):
    """Convert image to base64 encoded string"""
    try:
        image = Image.open(uploaded_file)
        buffer = io.BytesIO()
        image.save(buffer, format=image.format)
        return base64.b64encode(buffer.getvalue()).decode('utf-8'), image.format
    except Exception as e:
        st.error(f"Image processing error: {str(e)}")
        return None, None

def generate_pdf_report(report_text, uploaded_file):
    """Generate professionally formatted PDF report with bold headers."""
    buffer = io.BytesIO()
    doc = SimpleDocTemplate(buffer, pagesize=letter, 
                           rightMargin=72, leftMargin=72,
                           topMargin=72, bottomMargin=72)
    
    # Create custom styles for different parts of the report
    styles = getSampleStyleSheet()
    
    # Custom styles for better formatting
    title_style = ParagraphStyle(
        'ReportTitle',
        parent=styles['Title'],
        fontSize=16,
        alignment=1,  # Center aligned
        spaceAfter=12
    )
    
    header_style = ParagraphStyle(
        'SectionHeader',
        parent=styles['Heading2'],
        fontSize=12,
        fontName='Helvetica-Bold',
        textColor=colors.black,
        spaceBefore=12,
        spaceAfter=6
    )
    
    normal_style = ParagraphStyle(
        'NormalText',
        parent=styles['BodyText'],
        fontSize=11,
        leading=14,
        spaceAfter=8
    )
    
    abnormal_style = ParagraphStyle(
        'AbnormalText',
        parent=styles['BodyText'],
        fontSize=11,
        leading=14,
        textColor=colors.red,
        backColor=colors.lightgrey,
        borderPadding=5,
        spaceAfter=8
    )
    
    footer_style = ParagraphStyle(
        'FooterText',
        parent=styles['Italic'],
        fontSize=9,
        alignment=1  # Center aligned
    )
    
    # Begin building the report
    story = []
    
    # Hospital/Institution Header
    header_text = "RADIOLOGY DEPARTMENT"
    header = Paragraph(header_text, title_style)
    story.append(header)
    
    # Report Title
    report_title = "RADIOLOGICAL EXAMINATION REPORT"
    title = Paragraph(report_title, title_style)
    story.append(title)
    
    # Add date and report ID
    date_text = f"Date: {datetime.now().strftime('%B %d, %Y')}"
    report_id = f"Report ID: RAD-{datetime.now().strftime('%Y%m%d')}-{random.randint(1000, 9999)}"
    date_para = Paragraph(date_text, normal_style)
    id_para = Paragraph(report_id, normal_style)
    story.append(date_para)
    story.append(id_para)
    story.append(Spacer(1, 12))
    
    # Add the image to the PDF
    if uploaded_file:
        try:
            uploaded_file.seek(0)  # Reset file pointer to beginning
            pil_image = Image.open(uploaded_file)
            img_width = 5 * inch
            aspect = float(pil_image.height) / float(pil_image.width)
            img_height = img_width * aspect
            
            img_temp = io.BytesIO()
            pil_image.save(img_temp, format=pil_image.format if pil_image.format else 'JPEG')
            img_temp.seek(0)
            
            img = ReportLabImage(img_temp, width=img_width, height=img_height)
            story.append(img)
            story.append(Spacer(1, 12))
            
            # Add image caption
            caption = Paragraph("Figure 1: Radiological Image for Analysis", normal_style)
            story.append(caption)
            story.append(Spacer(1, 12))
            
        except Exception as e:
            error_text = Paragraph(f"Image processing error: {str(e)}", normal_style)
            story.append(error_text)
            story.append(Spacer(1, 12))
    
    # Clean the report text (remove markdown-style formatting and unwanted characters)
    cleaned_text = report_text.replace('**', '').replace('##', '').replace('*', '-')
    
    # Define section headers to identify
    section_headers = [
        "DIAGNOSIS",
        "ETIOLOGY",
        "RISK FACTORS",
        "PATHOPHYSIOLOGY",
        "CLINICAL FEATURES",
        "SIGNS AND SYMPTOMS",
        "INVESTIGATIONS",
        "MANAGEMENT",
        "INITIAL STABILIZATION",
        "MEDICAL MANAGEMENT",
        "SURGICAL MANAGEMENT",
        "PROGNOSIS"
    ]
    
    # Split into lines for more precise processing
    lines = cleaned_text.split('\n')
    current_section = ""
    section_content = ""
    
    for i, line in enumerate(lines):
        line = line.strip()
        if not line:
            continue
            
        # Remove any "Step X:" prefixes
        line = re.sub(r'^Step \d+:\s*', '', line)
        
        # Check if this is a section header
        is_header = False
        for header in section_headers:
            if line.upper().startswith(header) or line.upper() == header + ":":
                is_header = True
                break
                
        # Also check if it's a short line ending with a colon (likely a header)
        if not is_header and len(line) < 60 and line.endswith(':'):
            is_header = True
        
        # If we found a header 
        if is_header:
            # First add any accumulated content from previous section
            if section_content.strip():
                # Check for severe abnormalities to highlight
                severe_abnormal_keywords = [
                    'severe', 'critical', 'urgent', 'emergency', 'life-threatening',
                    'malignant', 'neoplasm', 'carcinoma', 'metastasis', 'hemorrhage',
                    'fracture', 'rupture', 'perforation',
                ]
                
                has_severe_issue = any(keyword in section_content.lower() for keyword in severe_abnormal_keywords)
                
                if current_section.upper().startswith("DIAGNOSIS") or current_section.upper().startswith("ABNORMAL"):
                    # This is a diagnosis section - highlight abnormalities
                    p = Paragraph(section_content, abnormal_style if has_severe_issue else normal_style)
                else:
                    p = Paragraph(section_content, normal_style)
                    
                story.append(p)
                story.append(Spacer(1, 6))
                section_content = ""
            
            # Add the new section header - remove any trailing colon for cleaner look
            clean_header = line.strip()
            if clean_header.endswith(':'):
                clean_header = clean_header[:-1]
                
            current_section = clean_header
            p = Paragraph(f"<b>{clean_header}</b>", header_style)  # Bold the header
            story.append(p)
            
        else:
            # This is content - append to the current section content
            if section_content:
                section_content += "<br/>" + line
            else:
                section_content = line
    
    # Add any remaining content
    if section_content.strip():
        p = Paragraph(section_content, normal_style)
        story.append(p)
    
    # Add conclusion if not present
    if not any("PROGNOSIS" in line.upper() for line in lines):
        conclusion_header = Paragraph("<b>PROGNOSIS</b>", header_style)
        story.append(conclusion_header)
        story.append(Spacer(1, 6))
        
        conclusion_text = "Prognosis varies based on the extent and location of findings. Clinical correlation with the patient's symptoms and medical history is recommended."
        conclusion_para = Paragraph(conclusion_text, normal_style)
        story.append(conclusion_para)
    
    # Add footer with disclaimer
    story.append(Spacer(1, 24))
    disclaimer = "This report was generated with AI assistance and should be reviewed by a qualified healthcare professional."
    footer = Paragraph(disclaimer, footer_style)
    story.append(footer)
    
    # Build PDF
    doc.build(story)
    buffer.seek(0)
    return buffer



        


def generate_radiology_report_groq(uploaded_file, client):
    """Generate AI-powered radiology analysis using Groq API"""
    base64_image, img_format = process_image_data(uploaded_file)
    
    if not base64_image:
        return None
    
    image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
    
    try:
        with st.spinner("Analyzing image..."):
            # Add progress bar for visual feedback
            progress_bar = st.progress(0)
            for i in range(100):
                # Update progress bar
                progress_bar.progress(i + 1)
                import time
                time.sleep(0.025)  # Simulate processing time
                
            # Updated prompt to request the detailed, structured format
            response = client.chat.completions.create(
                model="meta-llama/llama-4-maverick-17b-128e-instruct",  # Use Groq's model
                messages=[{
                    "role": "user",
                    "content": [
                        {"type": "text", "text": (
                            """As a radiologist, analyze the following Medical report and provide a comprehensive report structured as follows:

1. **DIAGNOSIS**: Clearly state the primary diagnosis, including dimensions in mmIf where applicable (e.g., if a tumor is present). Use specific anatomical terms relevant to the body part being examined.If it is chest xray also mention if pneumonia is present or not.

2. **FINDINGS**: 
   - Provide detailed observations from the Medical report, including:
     - The size, shape, and location of any lesions or abnormalities if applicable.
     - Description of the surrounding tissues and structures if applicable.
     - Any noted changes in signal intensity on various sequences (e.g., T1W, T2W, FLAIR) if applicable.
     - Mention of any associated findings, such as edema, mass effect, or midline shift if applicble.
     - Specific comments on vascular structures, if applicable.

3. **PATHOPHYSIOLOGY**: Briefly explain the disease mechanism related to the diagnosis, focusing on how it affects the specific body part.

4. **CLINICAL FEATURES**: Provide an overview of typical clinical presentations associated with this diagnosis, emphasizing symptoms that may arise from abnormalities in the specified anatomical area.

5. **SIGNS AND SYMPTOMS**: List common signs and symptoms relevant to the findings in the MRI report. Tailor this section to align with the specific anatomy being assessed.

6. **INVESTIGATIONS**: Mention diagnostic tests typically used for confirmation of the diagnosis, including imaging studies or laboratory tests pertinent to the body part.

7. **MANAGEMENT**: Outline the management plans in three parts:
   - Initial Stabilization: Describe immediate steps for patient care.
   - Medical Management: Outline pharmacological treatments and monitoring.
   - Surgical Management (if applicable): Discuss any surgical interventions specific to the diagnosis and body part.

8. **PROGNOSIS**: Describe expected outcomes and factors that may affect prognosis based on the diagnosis. Include considerations specific to the anatomical region and associated complications.

Please ensure to focus on the following findings from the report:
- Mention specific abnormalities based on the region (e.g., "T2/FLAIR hyperintensities in the right fronto-parietal region" for brain MRI).
- Highlight any significant lesions or deviations from the norm.
- Include any other abnormal findings noted in the report that are relevant to the specific anatomy.

Format each section with appropriate headings and use bullet points for lists. Base your analysis on the provided MRI report details."""
                        )},
                        {"type": "image_url", "image_url": {"url": image_url}},
                    ]
                }],
                temperature=0.1,
                max_tokens=3000,  # Increased token limit for more detailed response
                top_p=0.3
            )
            return response.choices[0].message.content
    except Exception as e:
        st.error(f"Groq API error: {str(e)}")
        return None

def generate_radiology_report(uploaded_file, api_choice='groq'):
    client = initialize_groq_client()
    if client:
        return generate_radiology_report_groq(uploaded_file, client)
    else:
        st.error("Failed to initialize Groq client. Please check your API key.")
        return None

# ======================
# UI COMPONENTS
# ======================
def display_animated_logo():
    """Display an animated medical logo"""
    logo_b64 = encode_logo("src/Round_image_depicting_a_futuristic_medical_image_a-1742282117033-photoaidcom-cropped.png")
    
    # If logo file doesn't exist, use the placeholder from encode_logo
    st.markdown(
        f"""
        <div class="logo-container">
            <div class="logo-pulse">
                <img src="data:image/png;base64,{logo_b64}" width="200">
            </div>
        </div>
        """, 
        unsafe_allow_html=True
    )

def display_main_interface():
    """Render primary application interface"""
    # Display animated logo and titles
    display_animated_logo()
    
    st.markdown('<h1 class="main-title">Radiology Analyzer</h1>', unsafe_allow_html=True)
    st.markdown('<p class="sub-title">Advanced Medical Imaging Analysis</p>', unsafe_allow_html=True)
    
    # Action buttons
    if st.session_state.get('analysis_result'):
        st.markdown(
            """
            <div class="analysis-complete">
                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" viewBox="0 0 16 16">
                    <path d="M16 8A8 8 0 1 1 0 8a8 8 0 0 1 16 0zm-3.97-3.03a.75.75 0 0 0-1.08.022L7.477 9.417 5.384 7.323a.75.75 0 0 0-1.06 1.06L6.97 11.03a.75.75 0 0 0 1.079-.02l3.992-4.99a.75.75 0 0 0-.01-1.05z"/>
                </svg>
                Analysis Complete
            </div>
            """,
            unsafe_allow_html=True
        )
        
        col1, col2 = st.columns([1, 1])
        with col1:
            pdf_report = generate_pdf_report(st.session_state.analysis_result, st.session_state.uploaded_file)
            st.download_button(
                label="📄 Download PDF Report",
                data=pdf_report,
                file_name="radiology_report.pdf",
                mime="application/pdf",
                use_container_width=True,
                help="Download formal PDF version of the report"
            )

        with col2:
            if st.button("Clear Analysis 🗑️", use_container_width=True, help="Remove current results"):
                st.session_state.pop('analysis_result', None)
                st.session_state.pop('uploaded_file', None)
                st.rerun()

        # Display analysis results in a styled container
        st.markdown("### 🎯 Radiological Findings Report")
        st.markdown(
            f'<div class="report-container"><div class="report-text">{st.session_state.analysis_result}</div></div>', 
            unsafe_allow_html=True
        )
    else:
        # Show a centered placeholder message
        st.markdown(
            """
            <div style="text-align: center; margin-top: 50px; color: #9ca3af; padding: 100px 0;">
                <svg xmlns="http://www.w3.org/2000/svg" width="64" height="64" fill="currentColor" viewBox="0 0 16 16" style="margin-bottom: 20px;">
                    <path d="M8 0a8 8 0 1 0 0 16A8 8 0 0 0 8 0ZM1.5 8a6.5 6.5 0 1 1 13 0 6.5 6.5 0 0 1-13 0Zm4.879-2.773 4.264 2.559a.25.25 0 0 1 0 .428l-4.264 2.559A.25.25 0 0 1 6 10.559V5.442a.25.25 0 0 1 .379-.215Z"/>
                </svg>
                <p style="font-size: 1.2rem;">Upload a medical image to begin analysis</p>
            </div>
            """,
            unsafe_allow_html=True
        )

def render_sidebar():
    """Create sidebar interface elements"""
    with st.sidebar:
        st.markdown('<div class="sidebar-item">', unsafe_allow_html=True)
        st.markdown('<div class="sidebar-title">Diagnostic Capabilities</div>', unsafe_allow_html=True)
        st.markdown("""
        - **Multi-Modality Analysis:** X-ray, MRI, CT, Ultrasound
        - **Pathology Detection:** Fractures, tumors, infections
        - **Comparative Analysis:** Track disease progression
        - **Structured Reporting:** Standardized output format
        - **Clinical Correlation:** Suggested next steps
        """)
        st.markdown("""
        <div class="tooltip">
            <strong>Disclaimer:</strong> This service does not provide medical advice.
            <span class="tooltiptext">Always consult with a qualified healthcare professional for diagnosis and treatment.</span>
        </div>
        """, unsafe_allow_html=True)
        st.markdown('</div>', unsafe_allow_html=True)        

        
        st.markdown('</div>', unsafe_allow_html=True)
        
        # Image Upload Section
        st.markdown('<div class="sidebar-item">', unsafe_allow_html=True)
        st.markdown('<div class="sidebar-title">Image Upload Section</div>', unsafe_allow_html=True)
        
        
        uploaded_file = st.file_uploader(
            "Select Medical Image", 
            type=ALLOWED_FILE_TYPES,
            label_visibility="collapsed",
            help="Supported formats: PNG, JPG, JPEG"
        )

        if uploaded_file:
            st.session_state.uploaded_file = uploaded_file  # Store uploaded file in session state
            
            # Display image with a styled container
            # st.markdown('<div class="card">', unsafe_allow_html=True)
            st.image(Image.open(uploaded_file), 
                    caption="Uploaded Medical Image",
                    use_container_width=True)
            st.markdown('</div>', unsafe_allow_html=True)

        if st.button("▶️ Initiate Analysis", use_container_width=True):
                    # Use the selected API provider
                    api_choice = 'groq'
                    report = generate_radiology_report(uploaded_file, api_choice)
                    
                    if report:
                        st.session_state.analysis_result = report
                        st.rerun()
        st.markdown('</div>', unsafe_allow_html=True)


# ======================
# APPLICATION ENTRYPOINT
# ======================
def main():
    """Primary application controller"""
    # Check if dark mode is in session state, default to true
    if 'dark_mode' not in st.session_state:
        st.session_state.dark_mode = True
    
    configure_application()
    
    render_sidebar()
    display_main_interface()

if __name__ == "__main__":
    main()